Predicting the risks of multiple healthcare-related outcomes via joint comorbidity discovery

Abstract

A mapping matrix, which maps from original features of an electronic health record database to higher level latent factors, is initialized. For each of one or more target diseases, regression coefficients are updated over the higher level latent factors, based on said initialized mapping matrix, a data matrix containing said original features, and a label vector of corresponding responses. Said mapping matrix is updated based on said updated regression coefficients. Said steps of updating said regression coefficients and updating said mapping matrix are repeated until convergence is achieved, to obtain a final mapping matrix and a final set of regression coefficients.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of U.S. Provisional Application Ser. No. 62 / 024,446 filed Jul. 14, 2014, entitled Multi-Task Learning Framework for Joint Disease Risk Prediction and Comorbidity Discovery, the complete disclosure of which is expressly incorporated herein by reference in its entirety for all purposes.FIELD OF THE INVENTION[0002]The present invention relates to the electrical, electronic, and computer arts, and, more particularly, to healthcare, medical analytics, and the like.BACKGROUND OF THE INVENTION[0003]Clinical risk prediction, also known as risk stratification, is an essential component of modern clinical decision support systems. It is attracting more and more attention in the recent years thanks to the adoption of Electronic Health Record (EHR) systems. State-of-the-art machine learning algorithms have been applied to massive EHR databases and promising results have been reported across the board. Generally spe...

AI Technical Summary

Benefits of technology

The present invention can help identify and predict comorbidity (when a person has multiple diseases or health issues) with better accuracy.

Problems solved by technology

Most of the existing risk prediction models are single-task, which means that they only predict the risk of contracting one disease at a time.

This becomes a limitation when, in practice, a health care provider is dealing with two or more diseases that share common comorbidities, risk factors, symptoms, etc. and the goal is to estimate the risk of several different diseases that are related to one another, e.g. hypertension and heart disease, diabetes and cataract, depression and obesity, etc.

Single-task prediction models are not equipped to identify these associations across different tasks.

Predicting these risks separately will likely cause the loss of crucial medical insights, such as confounding risk factors or hidden causes.

Although multi-task learning has been extensively studied in the machine learning community, existing multi-task learning techniques cannot be directly applied to the problem of EHR-based risk prediction because the validity of each algorithm relies on the specific assumption it makes about task relatedness and these assumptions often fail to hold for many clinical applications.

The first assumption is often too strong for disease risk prediction due to the heterogeneity of diseases.

The second assumption could be too difficult to validate in practice given our limited knowledge about the target diseases.

This assumption is also too restrictive for our application because it is not necessarily true that all diseases share a meaningful common basis.

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